Cancer treatment is in the process of transitioning from the era of empirical treatments for all patients with a particular form of cancer, to a more individualized and consequently mechanism-based approach to patient care. The genetic background of individual patients affects susceptibility to cancer development, the probability of malignant progression, and the likelihood of responding to particular targeted therapies. The objective of this application is to develop an integrated systems genetics approach that exploits genetic diversity between mouse strains, and new network analysis tools to identify the genes and pathways involved in each of these stages of cancer development and treatment. Systems genetics, unlike traditional approaches to the analysis of disease that focus on single genes or proteins in isolation, attempts to integrate the complex interaction of many kinds of genetic and biological information - genomic DNA sequence, mRNA, microRNA and protein expression, and link these to cancer phenotypes. This project will use systems genetics to integrate genetic, gene expression and phenotypic data from a mouse model of skin cancer that has been used for over 40 years to dissect the biological and molecular events that are essential for initiation, promotion and progression to malignant, metastatic disease. The first Specific Aim is based on the premise that a prerequisite for understanding cancer susceptibility is an appreciation of how the normal host gene expression in the target tissue is controlled. This will be analyzed by gene expression network analysis of normal skin from interspecific backcross mice as well as from a series of recombinant inbred lines, to exploit both linkage analysis and haplotyping for expression quantitative trait locus (eQTL) mapping. These network approaches are capable of revealing expression motifs associated with tissue structure, as well as pathways linked to stem cells, the cell cycle, and inflammatory responses. The second Specific Aim will identify features of the normal genetic architecture of expression that are associated with susceptibility to malignant progression, and will also look for perturbations in networks within carcinomas to identify signaling pathways and genes that may provide novel therapeutic targets.
Specific Aim 3 will take steps towards individualized cancer treatment by investigating the role of a specific gene, FBXW7, in determining response of primary tumors to inhibitors of mTOR - one of the main players in a well characterized oncogenic pathway.
Specific Aim 4 involves a close collaboration with groups working on systems genetics of human cancer to identify molecular signatures of the response of metastatic tumors to inhibitors of the mTOR pathway.
A major goal of modern cancer research is to use the latest advances in genetics to develop Personalized Medicine for cancer sufferers. Some mouse strains are genetically resistant to cancer development, and may therefore be used to find the genes and pathways that make certain humans susceptible to cancer development or that make some people respond to certain drugs while others are resistant. This project will develop and test a mouse model of personalized cancer susceptibility and treatment.
|Halliwill, Kyle D; Quigley, David A; Kang, Hio Chung et al. (2016) Panx3 links body mass index and tumorigenesis in a genetically heterogeneous mouse model of carcinogen-induced cancer. Genome Med 8:83|
|Adams, Cassandra J; Yu, Jennifer S; Mao, Jian-Hua et al. (2016) The Trp53 delta proline (Trp53Î”P) mouse exhibits increased genome instability and susceptibility to radiation-induced, but not spontaneous, tumor development. Mol Carcinog 55:1387-96|
|Quigley, David A; Kandyba, Eve; Huang, Phillips et al. (2016) Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer. Cell Rep 16:1153-65|
|Quigley, David; Silwal-Pandit, Laxmi; Dannenfelser, Ruth et al. (2015) Lymphocyte Invasion in IC10/Basal-Like Breast Tumors Is Associated with Wild-Type TP53. Mol Cancer Res 13:493-501|
|Quigley, David (2015) Equalizer reduces SNP bias in Affymetrix microarrays. BMC Bioinformatics 16:238|
|Westcott, Peter M K; Halliwill, Kyle D; To, Minh D et al. (2015) The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517:489-92|
|McCreery, Melissa Q; Halliwill, Kyle D; Chin, Douglas et al. (2015) Evolution of metastasis revealed by mutational landscapes of chemically induced skin cancers. Nat Med 21:1514-20|
|Song, Ihn Young; Balmain, Allan (2015) Cellular reprogramming in skin cancer. Semin Cancer Biol 32:32-9|
|Huang, Phillips Y; Balmain, Allan (2014) Modeling cutaneous squamous carcinoma development in the mouse. Cold Spring Harb Perspect Med 4:a013623|
|SjÃ¶lund, Jonas; Pelorosso, Facundo G; Quigley, David A et al. (2014) Identification of Hipk2 as an essential regulator of white fat development. Proc Natl Acad Sci U S A 111:7373-8|
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